Combined effects of heat waves and pesticide pollution on zooplankton communities: Does the timing of stressor matter?

Most studies assessing the combined effects of chemical and non-chemical stressors on aquatic ecosystems have been based on synchronous stressor applications. However, asynchronous exposure scenarios may be more common in nature, particularly for pulsed stressors such as heatwaves and pesticide concentration peaks. In this study, we investigated the single and combined effects of the insecticide chlorpyrifos (CPF) and a heatwave (HW) on a zooplankton community representative of a Mediterranean coastal wetland using synchronous (CPF+HW) and asynchronous (HW→CPF and CPF→HW) exposure scenarios. CPF was applied at a concentration of 0.8 µg/L (single pulse), and the HW was simulated by a temperature increase of 8°C above the control temperature (20°C) for 7 days in freshwater microcosms. The interaction between stressors in synchrony resulted in synergistic effects at the population level (Daphnia magna) and additive at the community level. The partial reduction of sensitive species resulted in an abundance increase of competing species that were more tolerant to the evaluated stressors (e.g. Moina sp.). The asynchronous exposure scenarios resulted in a similar abundance decline of sensitive populations as compared to the synchronous one; however, the timing of stressor resulted in different responses in the long term. In the HW→CPF treatment, the D. magna population recovered at least one month faster than in the CPF+HW treatment, probably due to survival selection and cross-tolerance mechanisms. In the CPF→HW treatment, the effects lasted longer than in the CPF+HW, and the population did not recover within the experimental period, most likely due to the energetic costs of detoxification and effects on internal damage recovery. The different timing and magnitude of indirect effects among the tested asynchronous scenarios resulted in more severe effects on the structure of the zooplankton community in the CPF→HW treatment. Our study highlights the relevance of considering the order of stressors to predict the long-term effects of chemicals and heatwaves both at the population and community levels.

Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method?

BACKGROUND AND OBJECTIVE: Patient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA detection would enable complete monitoring standard observational methods do not allow. While model-based and machine learning PVA approaches exist, they have variable performance and can miss specific PVA events. This study compares a model and rule-based algorithm with a machine learning PVA method by retrospectively validating both methods using an independent patient cohort. METHODS: Hysteresis loop analysis (HLA) which is a rule-based method (RBM) and a tri-input convolutional neural network (TCNN) machine learning model are used to classify 7 different types of PVA, including: 1) flow asynchrony; 2) reverse triggering; 3) premature cycling; 4) double triggering; 5) delayed cycling; 6) ineffective efforts; and 7) auto triggering. Class activation mapping (CAM) heatmaps visualise sections of respiratory waveforms the TCNN model uses for decision making, improving result interpretability. Both PVA classification methods were used to classify incidence in an independent retrospective clinical cohort of 11 mechanically ventilated patients for validation and performance comparison. RESULTS: Self-validation with the training dataset shows overall better HLA performance (accuracy, sensitivity, specificity: 97.5 %, 96.6 %, 98.1 %) compared to the TCNN model (accuracy, sensitivity, specificity: 89.5 %, 98.3 %, 83.9 %). In this study, the TCNN model demonstrates higher sensitivity in detecting PVA, but HLA was better at identifying non-PVA breathing cycles due to its rule-based nature. While the overall AI identified by both classification methods are very similar, the intra-patient distribution of each PVA type varies between HLA and TCNN. CONCLUSION: The collective findings underscore the efficacy of both HLA and TCNN in PVA detection, indicating the potential for real-time continuous monitoring of PVA. While ML methods such as TCNN demonstrate good PVA identification performance, it is essential to ensure optimal model architecture and diversity in training data before widespread uptake as standard care. Moving forward, further validation and adoption of RBM methods, such as HLA, offers an effective approach to PVA detection while providing clear distinction into the underlying patterns of PVA, better aligning with clinical needs for transparency, explicability, adaptability and reliability of these emerging tools for clinical care.

Application of a cloud platform that identifies patient-ventilator asynchrony and enables continuous monitoring of mechanical ventilation in intensive care unit.

Background: Patient-ventilator asynchrony (PVA) frequently occurs in mechanically ventilated patients within the ICU and has the potential for harm. Depending solely on the health care team cannot accurately and promptly identify PVA. To address this issue, our team has developed a cloud-based platform for monitoring mechanical ventilation (MV), comprising the PVA-RemoteMonitor system and the 24-h MV analysis report. We conducted a survey to evaluate physicians' satisfaction and acceptance of the platform in 14 ICUs. Methods: Data from medical records, clinical information systems, and ventilators were uploaded to the cloud platform and underwent data processing. The data were analyzed to monitor PVA and displayed in the front-end. The 24-h analysis report for MV was generated for clinical reference. Critical care physicians in 14 hospitals' ICUs that involved in the platform participated in a questionnaire survey, among whom 10 physicians were interviewed to investigate physicians' acceptance and opinions of this system. Results: The PVA-RemoteMonitor system exhibited a high level of specificity in detecting flow insufficiency, premature cycle, delayed cycle, reverse trigger, auto trigger, and overshoot, with sensitivities of 90.31 %, 98.76 %, 99.75 %, 99.97 %, 100 %, and 99.69 %, respectively. The 24-h analysis report supplied essential data about PVA and respiratory mechanics. 86.2 % (75/87) of physicians supported the application of this platform. Conclusions: The PVA-RemoteMonitor system accurately identified PVA, and the MV analysis report provided guidance in controlling PVA. Our platform can effectively assist ICU physicians in the management of ventilated patients.

Knowledge and associated factors of healthcare professionals in detecting patient-ventilator asynchrony using waveform analysis at intensive care units of the federal public hospitals in Addis Ababa, Ethiopia, 2023.

BACKGROUND: The interaction between the patient and the ventilator is often disturbed, resulting in patient-ventilator asynchrony (PVA). Asynchrony can lead to respiratory failure, increased artificial ventilation time, prolonged hospitalization, and escalated healthcare costs. Professionals' knowledge regarding waveform analysis has significant implications for improving patient outcomes and minimizing ventilation-related adverse events. Studies investigating the knowledge of healthcare professionals on patient-ventilator asynchrony and its associated factors in the Ethiopian context are limited. Therefore, this study aimed to assess the knowledge of healthcare professionals about using waveform analysis to detect asynchrony. METHODS: A multicenter cross-sectional study was conducted on 237 healthcare professionals (HCPs) working in the intensive care units (ICUs) of federal public hospitals in Addis Ababa, Ethiopia, from December 2022 to May 2023. The data were collected using a structured and pretested interviewer-administered questionnaire. Then, the collected data were cleaned, coded, and entered into Epi data V-4.2.2 and exported to SPSS V-27 for analysis. After description, associations were analyzed using binary logistic regression. Variables with a P-value of < 0.25 in the bivariable analysis were transferred to the multivariable analysis. Statistical significance was declared using 95% confidence intervals, and the strengths of associations were reported using adjusted odds ratios (AORs). RESULTS: A total of 237 HCPs participated in the study with a response rate of 100%. Half (49.8%) of the participants were females. The mean age of the participants was 29 years (SD = 3.57). Overall, 10.5% (95% CI: 6.9-15.2) of the participants had good knowledge of detecting PVA using waveform analysis. In the logistic regression, the number of MV-specific trainings and the training site had a statistically significant association with knowledge of HCPs. HCPs who attended more frequent MV training were more likely to have good knowledge than their counterparts [AOR = 6.88 (95% CI: 2.61-15.45)]. Additionally, the odds of good knowledge among professionals who attended offsite training were 2.6 times higher than those among professionals trained onsite [AOR = 2.63 (95% CI: 1.36-7.98)]. CONCLUSION: The knowledge of ICU healthcare professionals about the identification of PVA using waveform analysis is low. In addition, the study also showed that attending offsite MV training and repeated MV training sessions were independently associated with good knowledge. Consequently, the study findings magnify the relevance of providing frequent and specific training sessions focused on waveform analysis to boost the knowledge of HCPs.

Premotor cortical beta synchronization and the network neuromodulation of externally paced finger tapping in Parkinson's disease.

Parkinson's disease (PD) is characterized by the disruption of repetitive, concurrent and sequential motor actions due to compromised timing-functions principally located in cortex-basal ganglia (BG) circuits. Increasing evidence suggests that motor impairments in untreated PD patients are linked to an excessive synchronization of cortex-BG activity at beta frequencies (13-30 Hz). Levodopa and subthalamic nucleus deep brain stimulation (STN-DBS) suppress pathological beta-band reverberation and improve the motor symptoms in PD. Yet a dynamic tuning of beta oscillations in BG-cortical loops is fundamental for movement-timing and synchronization, and the impact of PD therapies on sensorimotor functions relying on neural transmission in the beta frequency-range remains controversial. Here, we set out to determine the differential effects of network neuromodulation through dopaminergic medication (ON and OFF levodopa) and STN-DBS (ON-DBS, OFF-DBS) on tapping synchronization and accompanying cortical activities. To this end, we conducted a rhythmic finger-tapping study with high-density EEG-recordings in 12 PD patients before and after surgery for STN-DBS and in 12 healthy controls. STN-DBS significantly ameliorated tapping parameters as frequency, amplitude and synchrony to the given auditory rhythms. Aberrant neurophysiologic signatures of sensorimotor feedback in the beta-range were found in PD patients: their neural modulation was weaker, temporally sluggish and less distributed over the right cortex in comparison to controls. Levodopa and STN-DBS boosted the dynamics of beta-band modulation over the right hemisphere, hinting to an improved timing of movements relying on tactile feedback. The strength of the post-event beta rebound over the supplementary motor area correlated significantly with the tapping asynchrony in patients, thus indexing the sensorimotor match between the external auditory pacing signals and the performed taps. PD patients showed an excessive interhemispheric coherence in the beta-frequency range during the finger-tapping task, while under DBS-ON the cortico-cortical connectivity in the beta-band was normalized. Ultimately, therapeutic DBS significantly ameliorated the auditory-motor coupling of PD patients, enhancing the electrophysiological processing of sensorimotor feedback-information related to beta-band activity, and thus allowing a more precise cued-tapping performance.

Mowing increased community stability in semiarid grasslands more than either fencing or grazing.

A substantial body of empirical evidence suggests that anthropogenic disturbance can affect the structure and function of grassland ecosystems. Despite this, few studies have elucidated the mechanisms through which grazing and mowing, the two most widespread land management practices, affect the stability of natural grassland communities. In this study, we draw upon 9 years of field data from natural grasslands in northern China to investigate the effects of gazing and mowing on community stability, specifically focusing on community aboveground net primary productivity (ANPP) and dominance, which are two major biodiversity mechanisms known to characterize community fluctuations. We found that both grazing and mowing reduced ANPP in comparison to areas enclosed by fencing. Grazing reduced community stability by increasing the likelihood of single-species dominance and decreasing the relative proportion of nondominant species. In contrast, mowing reduced the productivity of the dominant species but increased the productivity of nondominant species. As a consequence, mowing improved the overall community stability by increasing the stability of nondominant species. Our study provides novel insight into understanding of the relationship between community species fluctuation-stability, with implications for ecological research and ecosystem management in natural grasslands.

The effect of target detection task on memory encoding varies in different stimulus onset asynchronies.

The attentional boost effect (ABE) and action-induced memory enhancement (AIME) suggest that memory performance for target-paired items is superior to that for distractor-paired items when participants performed a target detection task and a memory encoding task simultaneously. Though the memory enhancement has been well established, the temporal dynamics of how the target detection task influenced memory encoding remains unclear. To investigate this, we manipulated the stimulus onset asynchrony (SOA) between detection stimuli and the words to be memorized using a remember/know study-test paradigm, and we focused primarily on memory performance for the words that appeared after the detection response. The results showed that target-paired memory enhancement was robust from SOA = 0 s to SOA = 0.75 s, but was not significant when examined by itself in Experiment 1A or weakened in Experiment 2 and the conjoint analysis when SOA = 1 s, which were only observed in R responses. The post-response memory enhancement still existed when there was no temporal overlap between the word and target, similar to the magnitude of memory enhancement observed with temporal overlap. These results supported the view that target-paired memory enhancement (recollection rather than familiarity) occurred irrespective of whether the items appeared simultaneously with the targets or within a short period after the response, and the temporal overlap of the word and target was not necessary for post-response memory enhancement.

Reverse triggering ? a novel or previously missed phenomenon?

BACKGROUND: Reverse triggering (RT) was described in 2013 as a form of patient-ventilator asynchrony, where patient's respiratory effort follows mechanical insufflation. Diagnosis requires esophageal pressure (Pes) or diaphragmatic electrical activity (EAdi), but RT can also be diagnosed using standard ventilator waveforms. HYPOTHESIS: We wondered (1) how frequently RT would be present but undetected in the figures from literature, especially before 2013; (2) whether it would be more prevalent in the era of small tidal volumes after 2000. METHODS: We searched PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials, from 1950 to 2017, with key words related to asynchrony to identify papers with figures including ventilator waveforms expected to display RT if present. Experts labelled waveforms. 'Definite' RT was identified when Pes or EAdi were in the tracing, and 'possible' RT when only flow and pressure waveforms were present. Expert assessment was compared to the author's descriptions of waveforms. RESULTS: We found 65 appropriate papers published from 1977 to now, containing 181 ventilator waveforms. 21 cases of 'possible' RT and 25 cases of 'definite' RT were identified by the experts. 18.8% of waveforms prior to 2013 had evidence of RT. Most cases were published after 2000 (1 before vs. 45 after, p = 0.03). 54% of RT cases were attributed to different phenomena. A few cases of identified RT were already described prior to 2013 using different terminology (earliest in 1997). While RT cases attributed to different phenomena decreased after 2013, 60% of 'possible' RT remained missed. CONCLUSION: RT has been present in the literature as early as 1997, but most cases were found after the introduction of low tidal volume ventilation in 2000. Following 2013, the number of undetected cases decreased, but RT are still commonly missed. Reverse Triggering, A Missed Phenomenon in the Literature. Critical Care Canada Forum 2019 Abstracts. Can J Anesth/J Can Anesth 67 (Suppl 1), 1-162 (2020). https://doi-org.myaccess.library.utoronto.ca/ https://doi.org/10.1007/s12630-019-01552-z .

Buffering and phenological mismatch: A change of perspective.

The potential for climate change to disrupt phenology-mediated interactions in interaction networks has attracted considerable attention in recent decades. Frequently, studies emphasize the fragility of ephemeral seasonal interactions, and the risks posed by phenological asynchrony. Here, we argue that the fitness consequences of asynchrony in phenological interactions may often be more buffered than is typically acknowledged. We identify three main forms that buffering may take: (i) mechanisms that reduce asynchrony between consumer and resource; (ii) mechanisms that reduce the costs of being asynchronous; and (iii) mechanisms that dampen interannual variance in performance across higher organizational units. Using synchrony between the hatching of winter moth caterpillars and the leafing of their host-plants as a case study, we identify a wide variety of buffers that reduce the detrimental consequences of phenological asynchrony on caterpillar individuals, populations, and meta-populations. We follow this by drawing on examples across a breadth of taxa, and demonstrate that these buffering mechanisms may be quite general. We conclude by identifying key gaps in our knowledge of the fitness and demographic consequences of buffering, in the context of phenological mismatch. Buffering has the potential to substantially alter our understanding of the biotic impacts of future climate change-a greater recognition of the contribution of these mechanisms may reveal that many trophic interactions are surprisingly resilient, and also serve to shift research emphasis to those systems with fewer buffers and towards identifying the limits of those buffers.

The effects of real-time waveform analysis software on patient ventilator synchronization during pressure support ventilation: a randomized crossover physiological study.

BACKGROUND: Patient-ventilator asynchrony commonly occurs during pressure support ventilation (PSV). IntelliSync + software (Hamilton Medical AG, Bonaduz, Switzerland) is a new ventilation technology that continuously analyzes ventilator waveforms to detect the beginning and end of patient inspiration in real time. This study aimed to evaluate the physiological effect of IntelliSync + software on inspiratory trigger delay time, delta airway (Paw) and esophageal (Pes) pressure drop during the trigger phase, airway occlusion pressure at 0.1 s (P0.1), and hemodynamic variables. METHODS: A randomized crossover physiologic study was conducted in 14 mechanically ventilated patients under PSV. Patients were randomly assigned to receive conventional flow trigger and cycling, inspiratory trigger synchronization (I-sync), cycle synchronization (C-sync), and inspiratory trigger and cycle synchronization (I/C-sync) for 15 min at each step. Other ventilator settings were kept constant. Paw, Pes, airflow, P0.1, respiratory rate, SpO2, and hemodynamic variables were recorded. The primary outcome was inspiratory trigger and cycle delay time between each intervention. Secondary outcomes were delta Paw and Pes drop during the trigger phase, P0.1, SpO2, and hemodynamic variables. RESULTS: The time to initiate the trigger was significantly shorter with I-sync compared to baseline (208.9±91.7 vs. 301.4±131.7 msec; P = 0.002) and I/C-sync compared to baseline (222.8±94.0 vs. 301.4±131.7 msec; P = 0.005). The I/C-sync group had significantly lower delta Paw and Pes drop during the trigger phase compared to C-sync group (-0.7±0.4 vs. -1.2±0.8 cmH2O; P = 0.028 and - 1.8±2.2 vs. -2.8±3.2 cmH2O; P = 0.011, respectively). No statistically significant differences were found in cycle delay time, P0.1 and other physiological variables between the groups. CONCLUSIONS: IntelliSync + software reduced inspiratory trigger delay time compared to the conventional flow trigger system during PSV mode. However, no significant improvements in cycle delay time and other physiological variables were observed with IntelliSync + software. TRIAL REGISTRATION: This study was registered in the Thai Clinical Trial Registry (TCTR20200528003; date of registration 28/05/2020).

Chronotype, Time of Day, and Children's Cognitive Performance in Remote Neuropsychological Assessment.

Research on the influence of chronotype and time of day (TOD) on cognitive performance, especially in children, is limited. We explored potential interactive effects, hypothesizing that performance differs when comparing preferred vs. non-preferred TOD. In total, 76 morning-type (MT = 37) or evening-type (ET = 39) children from the third and fourth grades (48.7% girls; M age = 8.05; SD age = 0.51), identified through the Children Chronotype Questionnaire, completed two 30-min neuropsychological assessment sessions via videoconference on the first (9:00) or last hour (16:00) of the school day. The protocol included neuropsychological tests targeting memory, language, and attention/executive domains. The results revealed an interactive effect of medium size between chronotype and TOD on a Rapid Alternating Stimulus (Naming) Task. MT and ET performed faster in asynchrony conditions (morning for ET; afternoon for MT). Additionally, ET outperformed MT in a Backward Digit Span Task, irrespective of TOD. TOD also influenced performance on an Alternating Verbal Fluency Task, with both MT and ET children performing better in the morning. These results underscore the importance of chronotype and TOD in children's cognitive performance, particularly in working memory and verbal fluency. Children assessed during non-preferred TOD exhibited better performance on some cognitive tasks, challenging the assumption that optimal times always yield superior results.

An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection.

BACKGROUND AND OBJECTIVE: Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While traditional detection methods for PVAs rely on visual inspection by clinicians, in recent years, machine learning models are being developed to detect PVAs automatically. However, training these models requires large labeled datasets, which are difficult to obtain, as labeling is a labour-intensive and time-consuming task, requiring clinical expertise. Simulating the lung-ventilator interactions has been proposed to obtain large labeled datasets to train machine learning classifiers. However, the obtained data lacks the influence of different hardware, of servo-controlled algorithms, and different sources of noise. Here, we propose VentGAN, an adversarial learning approach to improve simulated data by learning the ventilator fingerprints from unlabeled clinical data. METHODS: In VentGAN, the loss functions are designed to add characteristics of clinical waveforms to the generated results, while preserving the labels of the simulated waveforms. To validate VentGAN, we compare the performance for detection and classification of PVAs when training a previously developed machine learning algorithm with the original simulated data and with the data generated by VentGAN. Testing is performed on independent clinical data labeled by experts. The McNemar test is applied to evaluate statistical differences in the obtained classification accuracy. RESULTS: VentGAN significantly improves the classification accuracy for late cycling, early cycling and normal breaths (p< 0.01); no significant difference in accuracy was observed for delayed inspirations (p = 0.2), while the accuracy decreased for ineffective efforts (p< 0.01). CONCLUSIONS: Generation of realistic synthetic data with labels by the proposed framework is feasible and represents a promising avenue for improving training of machine learning models.

Audiovisual speech asynchrony asymmetrically modulates neural binding.

Previous psychophysical and neurophysiological studies in young healthy adults have provided evidence that audiovisual speech integration occurs with a large degree of temporal tolerance around true simultaneity. To further determine whether audiovisual speech asynchrony modulates auditory cortical processing and neural binding in young healthy adults, N1/P2 auditory evoked responses were compared using an additive model during a syllable categorization task, without or with an audiovisual asynchrony ranging from 240 ms visual lead to 240 ms auditory lead. Consistent with previous psychophysical findings, the observed results converge in favor of an asymmetric temporal integration window. Three main findings were observed: 1) predictive temporal and phonetic cues from pre-phonatory visual movements before the acoustic onset appeared essential for neural binding to occur, 2) audiovisual synchrony, with visual pre-phonatory movements predictive of the onset of the acoustic signal, was a prerequisite for N1 latency facilitation, and 3) P2 amplitude suppression and latency facilitation occurred even when visual pre-phonatory movements were not predictive of the acoustic onset but the syllable to come. Taken together, these findings help further clarify how audiovisual speech integration partly operates through two stages of visually-based temporal and phonetic predictions.

Response of bacterial and micro-eukaryotic communities to spatio-temporal fluctuations of wastewater in full scale constructed wetlands.

How microbial communities respond to wastewater fluctuations is poorly understood. Full-scale surface flow constructed wetlands (SFCWs) were constructed for investigating microbial communities. Results showed that influent wastewater changed sediment bacterial community composition seasonally, indicating that a single bacterial taxonomic group had low resistance (especially, Actinobacteriota and Gammaproteobacteria). However, copy numbers of 16S rRNA, ammonia oxidizing archaea, ammonia oxidizing bacteria, nirS and nirK in the first stage SFCWs were 2.49 × 1010, 3.48 × 109, 5.76 × 106, 8.77 × 108 and 9.06 × 108 g-1 dry sediment, respectively, which remained stable between seasons. Moreover, decreases in the nitrogen concentration in wastewater, changed microbial system state from heterotrophic to autotrophic. Micro-eukaryotic communities were more sensitive to wastewater fluctuations than bacterial communities. Overall, results revealed that microbial communities responded to spatio-temporal fluctuations in wastewater through state changes and species asynchrony. This highlighted complex processes of wastewater treatment by microbial components in SFCWs.

Forb stability, dwarf shrub stability and species asynchrony regulate ecosystem stability along an experimental precipitation gradient in a semi-arid desert grassland.

Precipitation pattern changes may affect plant biodiversity, which could impact ecosystem stability. However, the effects of changes in precipitation regime on ecosystem stability and their potential mechanisms are still unclear. We conducted a 3-year field manipulation experiment with five precipitation treatments (-40%, -20%, 0% (CK), +20% and +40% of ambient growing season precipitation) in a semi-arid desert grassland to examine the effects of precipitation alterations on functional group stability, species asynchrony, and diversity, and the underlying mchanisms of ecosystem stability using structural equation modelling. Alterations in precipitation had different effects on community biomass and functional group biomass. Moreover, ecosystem stability was mainly driven by forb stability (path coefficient = 0.79). Changes in precipitation had significant effects on soil dissolved inorganic N (P < 0.01) further affecting ecosystem stability through species asynchrony (path coefficient = 0.25). Dwarf shrubs had a stabilizing effect on ecosystem stability (path coefficient = 0.32), mainly via deep roots. Ecosystem stability tended to be lower in the -40% (4.72) and +40% (2.74) precipitation treatments. The common reduction in species asynchrony and stability of forb and dwarf shrub functional groups resulted in lower ecosystem stability under the -40% treatment. The lower stability under the +40% treatment might be ascribed to unimproved dwarf shrub stability. Higher dwarf shrub and forb stability contributed to higher ecosystem stability under normal precipitation changes (±20% treatments) and CK. Species diversity was not a crucial driver of ecosystem stability. Our results indicate that precipitation alteration can regulate ecosystem stability via functional group stability (e.g. forb stability, dwarf shrub stability) and species asynchrony in a semiarid desert grassland.

A marine heatwave changes the stabilizing effects of biodiversity in kelp forests.

Biodiversity can stabilize ecological communities through biological insurance, but climate and other environmental changes may disrupt this process via simultaneous ecosystem destabilization and biodiversity loss. While changes to diversity-stability relationships (DSRs) and the underlying mechanisms have been extensively explored in terrestrial plant communities, this topic remains largely unexplored in benthic marine ecosystems that comprise diverse assemblages of producers and consumers. By analyzing two decades of kelp forest biodiversity survey data, we discovered changes in diversity, stability, and their relationships at multiple scales (biological organizational levels, spatial scales, and functional groups) that were linked with the most severe marine heatwave ever documented in the North Pacific Ocean. Moreover, changes in the strength of DSRs during/after the heatwave were more apparent among functional groups than both biological organizational levels (population vs. ecosystem levels) and spatial scales (local vs. broad scales). Specifically, the strength of DSRs decreased for fishes, increased for mobile invertebrates and understory algae, and were unchanged for sessile invertebrates during/after the heatwave. Our findings suggest that biodiversity plays a key role in stabilizing marine ecosystems, but the resilience of DSRs to adverse climate impacts primarily depends on the functional identities of ecological communities.

Grassland degradation amplifies the negative effect of nitrogen enrichment on soil microbial community stability.

Although nitrogen (N) enrichment is known to threaten the temporal stability of aboveground net primary productivity, it remains unclear how it alters that of belowground microbial abundance and whether its impact can be regulated by grassland degradation. Using data from N enrichment experiments at temperate grasslands with no, moderate, severe, and extreme degradation degrees, we quantified the temporal stability of soil microbial abundance (hereafter 'microbial community stability') using the ratio of the mean quantitative PCR to its standard deviation over 4 years. Both bacterial and fungal community stability sharply decreased when N input exceeded 30 g N m-2 year-1 in non-degraded grasslands, whereas a reduction in this threshold occurred in degraded grasslands. Microbial species diversity, species asynchrony, and species associations jointly altered microbial community stability. Interestingly, the linkages between plant and microbial community stability were strengthened in degraded grasslands, suggesting that plants and soil microbes might depend on each other to keep stable communities in harsh environments. Our findings highlighted the importance of grassland degradation in regulating the responses of microbial community stability to N enrichment and provided experimental evidence for understanding the relationships between plant and microbial community stability.

Evaluating infection risks in buses based on passengers' dynamic temporal and typical spatial scenarios: A case study of COVID-19.

Conventional buses, as an indispensable part of the urban public transport system, impose cross-infection risks on passengers. To assess differential risks due to dynamic staying durations and locations, this study considered four spatial distributions (i = 1-4) and six temporal scenarios (j = 1-6) of buses. Based on field measurements and a risk assessment approach combining both short-range and room-scale effects, risks are evaluated properly. The results showed that temporal asynchrony between infected and susceptible individuals significantly affects disease transmission rates. The Control Case assumes that infected and susceptible individuals enter and leave synchronously. However, ignoring temporal asynchrony scenarios, i.e., the Control Case, resulted in overestimation (+30.7 % to +99.6 %) or underestimation (-15.2 % to -69.9 %) of the actual risk. Moreover, the relative difference ratios of room-scale risks between the Control Case and five temporal scenarios are impacted by ventilation. Short-range risk exists only if infected and susceptible individuals have temporal overlap on the bus. Considering temporal and spatial asynchrony, a more realistic total reproduction number (R) can be obtained. Subsequently, the total R was assessed under five temporal scenarios. On average, for the Control Case, the total R was estimated to be +27.3 % higher than j = 1, -9.3 % lower than j = 2, +12.8 % higher than j = 3, +33.0 % lower than j = 4, and + 77.6 % higher than j = 5. This implies the need for a combination of active prevention and real-time risk monitoring to enable rigid travel demand and control the spread of the epidemic.

Extra Nestlings That Are Condemned to Die Increase Reproductive Success in Hoopoes.

AbstractThe adaptive value of routinely laying more eggs than can be successfully fledged has intrigued evolutionary biologists for decades. Extra eggs could, for instance, be adaptive as insurance against hatching failures. Moreover, because recent literature demonstrates that sibling cannibalism is frequent in the Eurasian hoopoe (Upupa epops), producing extra offspring that may be cannibalized by older siblings might also be adaptive in birds. Here, directed to explore this possibility in hoopoes, we performed a food supplementation experiment during the laying period and a clutch size manipulation during the hatching stage. We found that females with the food supplement laid on average one more egg than control females and that the addition of a close-to-hatch egg at the end of the hatching period increased the intensity of sibling cannibalism and enhanced fledging success in hoopoe nests. Because none of the extra nestlings from the experimental extra eggs survived until fledging, these results strongly suggest that hoopoes obtain fitness advantages by using temporarily abundant resources to produce additional nestlings that will be cannibalized. These results therefore suppose the first experimental demonstration of the nutritive adaptive function of laying extra eggs in vertebrates with parental care.

[A Review on Automatic Detection Algorithm for Patient-Ventilator Asynchrony during Mechanical Ventilation].

This study summarizes the application of automatic recognition technologies for patient-ventilator asynchrony (PVA) during mechanical ventilation. In the early stages, the method of setting rules and thresholds relied on manual interpretation of ventilator parameters and waveforms. While these methods were intuitive and easy to operate, they were relatively sensitive in threshold setting and rule selection and could not adapt well to minor changes in patient status. Subsequently, machine learning and deep learning technologies began to emerge and develop. These technologies automatically extract and learn data characteristics through algorithms, making PVA detection more robust and universal. Among them, logistic regression, support vector machines, random forest, hidden Markov models, convolutional autoencoders, long short-term memory networks, one-dimensional convolutional neural networks, etc., have all been successfully used for PVA recognition. Despite the significant advancements in feature extraction through deep learning methods, their demand for labelled data is high, potentially consuming significant medical resources. Therefore, the combination of reinforcement learning and self-supervised learning may be a viable solution. In addition, most algorithm validations are based on a single dataset, so the need for cross-dataset validation in the future will be an important and challenging direction for development.